使用强可分性的完全有限状态机测试
Complete FSM Testing Using Strong Separability
AI总结：
本文针对 apartness 在定量模型中的不足，提出强可分性概念，并将其融入 HSI 测试方法，首次证明了可为信息物理系统等定量模型生成完全测试套件。
摘要：
Apartness 是构造性数学中发展起来的一个概念，近年来在模型学习和基于模型的测试领域中重新成为一种用于区分状态的有力概念。我们指出了 apartness 在定量模型（如混合系统和随机系统）中存在的一些根本性缺陷。我们提出了一个与之密切相关的替代概念，称为强可分性（strong separability），并证明用它替代 apartness 可以解决上述缺陷。我们改编了一种著名的完全基于模型的测试方法，即 Harmonized State Identifiers（HSI）方法，使其采用所提出的强可分性概念。我们证明了改编后的 HSI 方法是完全的。据我们所知，这是首个展示如何为定量模型（例如在信息物理系统开发中所使用的那些模型）生成完全测试套件的工作。
英文摘要：
Apartness is a concept developed in constructive mathematics, which has resurfaced as a powerful notion for separating states in the area of model learning and model-based testing. We identify some fundamental shortcomings of apartness in quantitative models, such as in hybrid and stochastic systems. We propose a closely-related alternative, called strong separability and show that using it to replace apartness addresses the identified shortcomings. We adapt a well-known complete model-based testing method, called the Harmonized State Identifiers (HSI) method, to adopt the proposed notion of strong separability. We prove that the adapted HSI method is complete. As far as we are aware, this is the first work to show how complete test suites can be generated for quantitative models such as those found in the development of cyber-physical systems.
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